{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:41:29Z","timestamp":1705106489281},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>Maintenance plays a significant role in semiconductor manufacturing as plant yield, factory downtime and operation cost are all closely related to maintenance efficiency. Accordingly, maintenance strategies in semiconductor manufacturing industries are increasingly shifting from traditional preventive maintenance (PM) to more efficient predictive maintenance (PdM). PdM uses manufacturing process data to develop predictive models for remaining useful life (RUL) estimation of key equipment components. Traditional approaches to building predictive models for RUL estimation involve manual selection of features from manufacturing process data. This paper proposes to use deep convolutional neural networks (CNN) for the task of estimating RUL of lenses for an ion beam etch tool in semiconductor manufacturing. The proposed approach has the advantage of automatic feature extraction through the use of convolution and pool filters along the temporal dimension of the optical emission spectroscopy (OES) data from the endpoint detection system. Simulation studies demonstrate the feasibility and the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.3233\/faia231177","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:55:18Z","timestamp":1705064118000},"source":"Crossref","is-referenced-by-count":0,"title":["Remaining Useful Life Estimation of Lenses for an Ion Beam Etching Tool in Semiconductor Manufacturing Using Deep Convolutional Neural Networks"],"prefix":"10.3233","author":[{"given":"Jian","family":"Wan","sequence":"first","affiliation":[{"name":"Department of Mechanical, Biomedical and Design Engineering, School of Engineering and Technology, College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK"}]},{"given":"Se\u00e1n","family":"McLoone","sequence":"additional","affiliation":[{"name":"School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT9 5BN, UK"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:55:18Z","timestamp":1705064118000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231177","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}